Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global SaaS organization is building an Applied AI function focused on transforming how modern go-to-market teams operate. The organization combines advanced AI capabilities with deep commercial expertise to develop agents, intelligence layers, automations, and decision-support systems that improve revenue execution.
Key Responsibilities
- Conduct stakeholder research across the GTM lifecycle, from pre-sales through post-sales, to identify intelligence gaps, workflow friction, and handoff failures.
- Translate research findings into a structured and prioritized backlog of opportunities, including problem statements, impact and feasibility assessments, dependencies, and stakeholders.
- Build and refine AI agents tailored to specific areas of the revenue lifecycle.
- Contribute directly to system architecture, retrieval logic, output calibration, and shared AI infrastructure.
- Deliver working AI solutions against real-world commercial workflows.
- Remain embedded in regional operating rhythms, including pipeline reviews, deal cycles, quarterly business reviews, renewal planning, and account strategy sessions.
- Monitor the reliability, quality, and adoption of AI systems.
- Diagnose output failures and continuously tune and improve deployed solutions.
- Maintain ownership of systems after deployment rather than relying solely on operational handoffs.
- Contribute to a shared knowledge hub by validating field signals and structuring deal and customer patterns.
- Ensure intelligence captured across the revenue lifecycle improves future AI agent performance.
- Identify successful patterns and scale them across account scenarios, customer segments, and deal stages.
- Build reusable components and scalable frameworks for adjacent GTM workflows.
- Collaborate with Growth Engineering, Product Marketing, Solutions Consulting, Pricing, Customer Success, and leadership teams.
- Ensure AI systems are grounded in cross-functional context and adopted by the teams they support.
- Help define the future direction, operating model, and best practices of the Applied AI function.
Required Qualifications
- 6+ years of professional experience in a GTM role with direct involvement in the commercial lifecycle, such as Solutions Consulting, Solutions Architecture, Account Executive, Sales Leadership, GTM Strategy, or a comparable role within a B2B SaaS organization.
- Hands-on experience building AI-powered tools, agents, automations, or workflows that have transformed real business processes.
- Technical fluency in integration architecture, AI agent evaluation, prompt design, retrieval logic, and data workflows.
- Experience with technologies such as Python, APIs, integrations, and AI development platforms.
- Strong understanding of enterprise sales workflows, deal stages, and the operating realities of GTM teams.
- Experience engaging with executive-level customers and stakeholders.
- Demonstrated ability to take solutions from problem identification through implementation and field adoption.
- Ability to manage multiple workstreams while balancing field engagement, system development, and continuous refinement.
- Highly autonomous and comfortable working independently as a senior individual contributor.
- Product Management experience or a demonstrated product-oriented approach to building solutions that users actively adopt.
- Experience in Solutions Consulting, Solutions Engineering, or field architecture within an enterprise SaaS environment.
- Proficiency with AI development tools and platforms, including AI coding assistants, custom agent frameworks, API integrations, and workflow automation tools.
- Familiarity with enterprise SaaS technologies such as Salesforce, Gong, Slack, and Snowflake.
- Experience building and scaling internal tools or systems beyond an immediate team.
- Strong analytical capabilities, including the ability to evaluate deal data, diagnose AI output failures, and develop data-driven recommendation systems.
Disclaimer
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